Early detection of within-field yield variability for high-value commodity crops, such as cotton (Gossypium spp.), offers growers potential to improve decision-making, optimize yields, and increase profits. Over recent years, publicly available datasets have become increasingly available and at a resolution where within-field yield prediction is possible. However, the viability of using these datasets with machine learning to predict within-field cotton lint yield at key growth stages are largely unknown. This study was conducted on two cotton fields, located near Mungindi, New South Wales, Australia. Three years of yield data, soil, elevation, rainfall, and Landsat imagery were collected from each field. A total of 12 models were created using: (a) two machine learning algorithms: random forest (RF) and gradient boosting machines (GBM); (b) three growth stages: squaring, flowering, and boll-fill; and(c) two different amounts of variables: all variables and the optimal variables determined by a recursive feature elimination (RFE). Results showed a strong agreement between predicted and observed yields at flowering and boll-fill when more information was available. At flowering and boll-fill, root mean square error (RMSE) ranged between 0.15 and 0.20 t ha −1 and Lin's concordance correlation coefficient (LCCC) ranged between 0.50 and 0.66, with RF providing superior results in most cases. Models created using the optimal variables determined by the RFE provided similar results compared to using all variables, allowing greater model accuracy and resolution with targeted sampling. Overall, these findings indicate significant potential of publicly available datasets to predict within-field cotton yield and guide decisionmaking in-season.

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Biologi merupakan salah satu bidang ilmu dalam kurikulum pendidikan tingkat SMA dengan banyak istilah yang sulit dipahami oleh siswa, terutama pada topik mengenai bagian-bagian tumbuhan seperti bunga, hal ini disebabkan adanya perbedaan kondisi fisik saat diamati sehingga siswa mendapatkan gambaran yang berbeda. Selain itu, guru di sekolah masih hanya menggunakan metode dalam pengajarannya dengan menjelaskan materi kemudian siswa mengerjakan soal, jadi lebih berfokus ke penghafalan. Dalam penelitian ini pengenalan bagian-bagian bunga diimplementasikan menjadi aplikasi berbasis mobile dan web yang dapat mengatasi permasalahan tersebut dengan menjabarkan bagian dari bunga berbasis Augmented Reality (AR). Aplikasi mobile yang dikembangkan menggunakan bahasa pemrograman Java dan C#, sedangkan aplikasi web menggunakan CodeIgniter sebagai backend dan Bootstrap sebagai front-end.Dari hasil pengujian sistem menggunakan blackbox testing didapatkan aplikasi pembelajaran morfologi bunga berbasis augmented reality berfungsi dengan baik dan diharapkan dapat mempermudah siswa dalam memahami bagian-bagian bunga beserta nama dan fungsinya, serta memudahkan guru dalam menyampaikan materi mengenai bagian-bagian bunga.

Agricultural fields are inherently variable across both space and time but are commonly managed uniformly. Uniform management can simultaneously lead to an under and over-application of resources (e.g. fertiliser) within the same field, resulting in poor resource efficiency and reduced profit margins. This research demonstrated the potential of publicly available datasets (i.e. remote sensing, digital soil maps, weather), machine learning techniques and crop models to inform management at a sub-paddock scale. These findings will help provide a cost-effective and efficient approach to improving farm productivity, profitability and sustainability in Australian irrigated cotton systems.